Food choices can vary strongly across time even for stable
environments and contexts, thus violating axioms of transitivity.
Classical behavioral choice models from
psycholo-gy and economics subsume this variability in
unspecific noise terms that have no clearly defined mental basis. In this talk,
I propose that the variability of food choices can emerge naturally
from the probabilistic nature of value computations instantiated by
neuronal populations in the ventro-medial prefrontal
cortex, and by the properties of the mechanisms by which these
computed values are conveyed to other choice-related areas. I will first show
that distributed patterns of neural activity in the vmPFC, as measured with
fMRI, encode probability distributions over stimulus values and that
this probabilistic information can be used to derive
estimates of both the preferences themselves and of the associated
uncertainty.

I will then present a biologically-grounded model of these
computations that allows organisms to access and exploit the
uncertainty associated with their preferences in order to optimally guide
value-based choices. This model accurately predicts the
outcome and response speed of preference-based decisions and is
able to explain the emergence of framing-related choice biases. This
proposed coding scheme makes it possible for humans to optimally combine
multiple sources of information for decisions and may pave the way
for mechanistic explanations of puzzling distortions often observed in food-
and non-food-related economic choices.

Finally, I will show with combination of
EEG and tACS data that gamma-band neural coherence between
(presumably) medial prefrontal and parietal areas can be causally
required for a stable incorporation of these computed values into choices.
This suggests variability in information transmission in neural
networks as a second possible neural origin of
the variability of value-based choices.